Search Results (281)

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

This study examines the time–frequency dynamics between CO₂ emissions and their determinants—oil prices, renewable energy deployment, and climate policy uncertainty—in Türkiye from 1987Q2 to 2024Q1. We integrate a rolling-window Nonlinear Autoregressive Distributed Lag (NARDL) model with wavelet coherence analysis to capture evolving asymmetric effects and multi-scale transmission mechanisms. Our findings reveal pronounced, persistent asymmetries. Oil price decreases stimulate CO₂ emissions substantially more than equivalent price increases reduce them, yielding a negative asymmetry effect. Renewable energy demonstrates a stable, negative long-run relationship with emissions, with wavelet analysis indicating this effect concentrates over medium-to-long-term horizons, underscoring its structural decarbonization role. Climate policy uncertainty exerts fragmented, episodic influences, disrupting short-to-medium-term emission trajectories. Rolling-window estimates confirm these asymmetric relationships shift markedly around structural breaks, including the 2001 domestic crisis and the 2008 global financial crisis. The study concludes that effective decarbonization requires temporally calibrated policies: counter-cyclical carbon pricing to offset oil price asymmetries, and credible long-term frameworks to sustain renewable energy investments. Methodologically, the results demonstrate the value of combining time-domain and frequency-domain techniques to diagnose complex, evolving interactions in the energy–environment nexus. Full article

Background: Hippotherapy, a sensorimotor-rich intervention proposed for children with Autism Spectrum Disorder (ASD), is suggested to influence executive function (EF). However, the underlying electrophysiological mechanisms, particularly changes observed in resting-state Electroencephalography (EEG), remain underexplored. Methods: A total of forty-eight children with ASD, aged 9–12 years, participated in this quasi-experimental, non-randomized pre-test–post-test study. Participants were assigned to either a standardized 12-session hippotherapy program (n = 24) or a waitlist Control group (n = 24). EF was evaluated pre- and post-intervention using validated measures: the Wisconsin Card Sorting Test, Stroop Color–Word Test, Corsi Block-Tapping Task, and Tower of London. Resting-state EEG data (19 channels, 250 Hz) were recorded before and after the intervention and analyzed for spectral power, pairwise Pearson correlation, phase-based functional connectivity using the Phase Lag Index (PLI), and directed effective connectivity using Phase Transfer Entropy (PTE). EEG effects were tested with linear mixed models in MATLAB (fitlme), with the measured values in each ROI as the dependent variable, group and time as fixed effects, and SubjectID included as a random intercept; EF outcomes were analyzed with ANCOVA/MANCOVA, adjusting post-test scores for baseline. The assumptions of homogeneity of slopes, Levene’s test, and the Shapiro–Wilk test were examined, and the Holm–Bonferroni correction together with partial η² effect sizes were reported. Results: Following baseline adjustment, the hippotherapy group showed substantial and statistically significant improvements across all EF measures compared with controls partial η² range = 0.473–0.855; all adjusted p < 0.001; e.g., Stroop Incongruent Reaction Time (F(1,45) = 265.80, p < 0.001, ηp² = 0.855). EEG analyses revealed localized Group × Time interaction effects involving frontal delta power as well as selected alpha-, theta-, and beta-band connectivity measures within frontally anchored networks. In addition to these focal interaction effects, the hippotherapy group exhibited a narrower distribution of pre–post EEG changes across spectral power and connectivity metrics compared with controls, indicating greater temporal consistency in resting-state electrophysiological dynamics across sessions. Because group allocation was non-random (based on scheduling feasibility and parental preference), results should be interpreted as associations rather than causal effects. While the hippotherapy group exhibited significant EF improvements and relative stabilization in EEG spectral and connectivity metrics, particularly in frontal delta/theta/alpha/beta bands, a direct mapping between individual EEG changes and behavioral gains was not observed. Conclusions: A standardized 12-session hippotherapy program was associated with substantial improvements in EF and with relative stabilization of resting-state electrophysiological dynamics in children with ASD. However, the direct mechanistic link between these EEG and behavioral changes warrants further investigation. Larger randomized trials employing active control conditions, task-evoked electrophysiological measures, and extended longitudinal follow-up are needed to confirm efficacy, clarify mechanisms, and establish the durability of effects. Full article

A strong transportation infrastructure is critical in advancing ICT trade by facilitating the efficient movement of goods and services. This efficiency enhances supply chains and attracts greater foreign direct investment, ultimately supporting technological development and boosting the economy. This article evaluates the relationship between transportation infrastructure (TI), information and communication technology trade openness (ICT trade), foreign direct investment (FDI), and economic growth (GDP) in Saudi Arabia from 1990 to 2023. Using the Autoregressive Distributed Lag (ARDL) model, we found that ICT trade has a statistically significant positive effect on long-run GDP growth. However, in the short run, ICT trade has a positive but non-significant impact on GDP growth. Additionally, the results show that TI has a statistically significant negative effect on short-run GDP growth. Moreover, the non-linear Threshold Regression model results show a threshold value for information and communication technology trade openness (ICT trade) of approximately 0.4051. Specifically, the findings indicate that increased ICT trade reduces the negative impact on economic growth beyond a certain threshold. This study is highly significant for Saudi Arabian decision-makers, as it highlights the roles of transportation infrastructure and ICT trade in attracting FDI and bolstering the economy. Full article

This study examines the asymmetric responses of stock market indices in Türkiye and Kazakhstan to oil price shocks during the 2010–2025 period. Using the Nonlinear Autoregressive Distributed Lag (NARDL) model, the study decomposes the nonlinear effects of oil price fluctuations on financial markets. Empirical findings reveal that in Türkiye, a net oil importer, the stock market exhibits a dual-sensitivity: while exchange rate dynamics (2.34) remain the dominant driver, oil price increases (−0.12) exert a direct and statistically significant negative pressure. In contrast, Kazakhstan, a net oil exporter, shows a high vulnerability to oil price decreases (−1.05) at the 1% significance level, confirming a strong asymmetric structure (p = 0.0122). Furthermore, the error correction speed is significantly higher in Türkiye (28%) than in Kazakhstan (4%), indicating divergent market efficiency and recovery mechanisms. These results demonstrate that financial market reactions to external shocks differ fundamentally based on energy trade structures. The findings suggest that oil-importing countries must prioritize exchange rate stability, while oil-exporting nations must develop specific policy buffers against the persistent downside risks of global energy cycles. Full article

This study investigates whether fluctuations in the real exchange rate give rise to symmetric or asymmetric J-curve effects in manufacturing trade between Türkiye and Germany, thereby positioning the analysis within and contributing to the broader scholarly discourse on exchange rate–trade balance dynamics. Using monthly data for the period 2013M01–2025M07, the paper first estimates a linear Autoregressive Distributed Lag (ARDL) model for the bilateral manufacturing trade balance and subsequently extends the framework to a nonlinear ARDL (NARDL) specification, which explicitly incorporates symmetry and asymmetry by decomposing real exchange rate changes into positive (depreciation) and negative (appreciation) partial sums. The linear ARDL results provide no evidence of a conventional J-curve and suggest that the aggregate impact of the real exchange rate is weak and often statistically insignificant. In contrast, the NARDL estimates uncover pronounced long-run and cumulative short-run asymmetries: real depreciations of the Turkish lira are associated with a persistent improvement in the bilateral manufacturing trade balance, whereas appreciations exert weak and statistically insignificant effects, a finding that remains robust when a real effective exchange rate measure is employed. Overall, the evidence indicates that Türkiye–Germany manufacturing trade does not conform to the standard J-curve pattern. These findings suggest that trade policy should adopt an asymmetric stance toward exchange rate movements: since depreciations yield persistent trade balance improvements while appreciations produce negligible effects, policies designed to support export competitiveness should prioritize the management of depreciation episodes rather than assuming symmetric adjustment dynamics. Full article

Older adults are particularly vulnerable to extreme heat, but evidence of the role of social factors in regional heat vulnerability remains limited. To assess the impacts of heat waves on cardiorespiratory hospitalizations and mortality, we developed a Climate Vulnerability Index by the Regional Health Department (RHD), including adults aged ≥ 60 years across 17 RHDs in São Paulo State, Brazil. Health data were obtained from national information systems, and heat wave exposure was derived from ERA5 reanalysis data, defined as periods of at least three consecutive days with daily mean temperature exceeding the seasonal climatological mean by ≥3 °C, for 2010–2019 and 2023–2024, excluding 2020–2022. Associations between heat waves and health outcomes were estimated using distributed lag non-linear models with lags of 0–15 days. Cumulative relative risks, along with sociodemographic, sanitation, and health system indicators, were integrated to construct the Index based on IPCC sensitivity and adaptive capacity domains. Heat waves were associated with increased risks of cardiorespiratory hospitalizations and mortality across all RHDs, with stronger effects observed for mortality and inland regions. Higher vulnerability was concentrated in RHDs characterized by larger older adult populations, greater heat-related risks, and weaker health system and sanitation indicators, whereas more developed regions showed lower vulnerability. Overall, the Index provides a practical tool to support territorial prioritization and targeted heat–health adaptation strategies in ageing populations. Full article

We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on ${(0,1)}^2$. To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. Full article

Given the context of the COVID-19 pandemic disrupting global logistics, coupled with the Russia–Ukraine war causing global energy price changes, examining both the linear and nonlinear associations between shipping cost and oil price is crucial in a global context. This study empirically exhibits the association among Global Commodity Prices Index (GPI), Oil Price (OP), Gold Future Price (GFP), and Baltic Dry Index (BDI) by employing Linear Autoregressive Distributive Lag (ARDL) as well as Nonlinear Autoregressive Distributive Lag (Nonlinear ARDL) from January 2003 to January 2023. The findings indicate that the influence of OP on BDI has a negative impact in the long run and a positive impact in the short run. Furthermore, the OP has an asymmetric effect on BDI in both the long and short terms. Finally, the predictive performance of the NARDL model outperforms the ARDL model in forecasting OP and BDI. The empirical findings derived from the ARDL and NARDL algorithms offer valuable insights for policymakers in designing public policies and for investors in portfolio construction. Full article

Preterm birth (PTB) has been increasingly linked to maternal exposure to fine particulate matter (PM_2.5) during pregnancy. However, the contribution of individual PM_2.5 constituents risk remains unclear. This research investigated associations between prenatal exposure to PM_2.5 constituents and PTB risk using a hypothetical intervention approach. A birth cohort of 148,068 mother–child pairs from Foshan, China was constructed from health records. Maternal exposure to PM_2.5 constituents—including black carbon (BC), organic matter (OM), nitrate (NO₃⁻), ammonium (NH₄⁺), and sulfate (SO₄²⁻)—was estimated based on satellite-derived spatial and temporal modeling. Parametric G-computation and distributed lag nonlinear models were used to estimate the cumulative risks of PTB under hypothetical reductions of PM_2.5 constituents during pregnancy. Potential benefits (preventable PTB cases) were also estimated. Among the cohort, 9757 (6.59%) PTBs were observed. Hypothetical reductions in all five constituents during pregnancy were associated with decreased cumulative risks of birth at week 36 (i.e., the threshold for PTB). For instance, a 40% reduction (reducing PM_2.5 to the WHO recommended levels) yielded risk differences of −2.29 (BC), −3.70 (OM), −4.74 (NH₄⁺), −5.00 (NO₃⁻), and −2.11 (SO₄²⁻) per thousand births, corresponding to 312 (3.20%) to 740 (7.58%) preventable cases. Our results indicate that reductions in PM_2.5 constituents, especially NO₃⁻ and NH₄⁺, were associated with lower risks of PTB. Full article

Accurate temperature control in ceramic roller kilns is critical for ensuring product quality; however, it remains challenging due to nonlinear thermal dynamics and the spatial lag inherent in traditional contact-based sensors. To address the limitations of sparse wall-mounted thermocouples and optical interference in kiln images, this paper presents a multimodal spatiotemporal fusion network (MST-FusionNet) for noncontact temperature detection of ceramic bodies on roller tracks. The proposed network integrates in-furnace combustion image sequences with distributed thermocouple measurements. First, a physics-informed pseudo-heatmap generation strategy based on Gaussian distributions is introduced to align discrete thermocouple readings with visual features, enabling effective early-stage multimodal fusion. Second, a residual compensation mechanism uses thermocouple data as a stable reference to learn local temperature deviations from visual and temporal features. In addition, an attention-enhanced LSTM module is employed to model combustion dynamics and suppress unreliable frames caused by smoke and flame fluctuations. Experimental results on a real industrial dataset show that the proposed method achieves a mean absolute error of 0.9164 °C and a root mean squared error of 1.2422 °C, demonstrating better performance than single-modal methods and simple fusion baselines. The proposed framework exhibits stable spatial characteristics across different roller positions and helps bridge the spatial discrepancy between boundary measurements and the actual thermal state of ceramic products, providing an effective solution for temperature detection in roller kilns. Full article

Background/Objectives: Meloxicam (MLX) is a nonsteroidal anti-inflammatory drug (NSAID) recommended for treating acute and chronic pain in dogs, frequently administered prophylactically to mitigate postoperative pain; however, its utility is limited by characteristic NSAID-associated adverse effects, such as gastrointestinal side effects. Nanosystems offer the potential to minimize adverse effects by sustaining drug release. Therefore, this study assessed the pharmacokinetics of MLX nanoencapsulation in female dogs undergoing ovariohysterectomy using a population pharmacokinetic (PopPK) modeling approach. Methods: MLX-loaded polymeric nanocapsules (NC-MLX) were prepared using the nanoprecipitation method and characterized by zeta potential, pH, mean diameter, particle size distribution, and drug content. Dogs received 0.2 mg/kg of either NC-MLX or free MLX orally, 4 h before surgery, and plasma samples were analyzed using an HPLC-PDA method. Pharmacokinetics were characterized by non-compartmental analysis and PopPK modeling. Several compartmental structures, variability models, and residual error models were explored, and relevant covariates were investigated. Results: NC-MLX had an average diameter of 326 ± 13 nm, a zeta potential of −26.2 ± 6.4 mV, and drug loading of 99.47% ± 0.01%. NC-MLX showed a significant increase in the t_1/2 (36.99 ± 17.26 h) of MLX compared to the free drug (15.22 ± 4.4 h). The best-fitting PopPK model was a two-compartment model with double extravascular first-order absorption rate constants (Ka₁ and Ka₂), including a lag time for Ka₂ and linear elimination, describing the second peak observed in several animals. The nanoformulation was a significant covariate for Tlag₂, delaying the time for absorption (1.22 and 2.55 h for free MLX and NC-MLX, respectively) and increasing V₂ (0.134 and 0.402 L/kg for free MLX and NC-MLX, respectively). External model validation showed that the final PopPK model accurately predicted plasma concentrations, with MPE% and RMSE values below 15%. Conclusions: Our findings suggest that NC-MLX alters MLX absorption and distribution profiles, supporting its potential as an alternative for postoperative pain management in dogs. Full article

To address the challenges of traditional methods for monitoring rock dynamic hazards in mines, which struggle to fully characterize the spatiotemporal heterogeneity of damage evolution and the resulting lag in early warning, this paper proposes a dynamic rock damage classification and fracture early warning model driven by acoustic emission data. Based on an improved dynamic K-means algorithm, this model fuses time dependence, energy intensity, and event spatial density characteristics through exponentially decaying weights to construct a spatiotemporal-energy synergistic clustering framework. Furthermore, a nonlinear coupling model for the comprehensive risk index (RI) is established, combining the static damage variable D with dynamic parameters such as energy release rate, ring count, and spatial clustering, to create a five-level early warning threshold. Experimental results demonstrate that the improved algorithm achieves clustering silhouette coefficients exceeding 0.7 for single-source, multi-source, and complex fracture patterns, and the error between cluster regions and actual fracture distribution is less than 1 mm. The RI model accurately identifies the damage state of the test block and effectively predicts critical instability, significantly improving both timeliness and accuracy. This research overcomes the limitations of traditional static evaluation and provides high-precision technical support for real-time monitoring of hidden rock fractures and prevention and control of mine dynamic hazards. Full article

Background/Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by persistent synovial inflammation and vascular abnormalities. Emerging evidence suggests that dysbiosis of the microbiome contributes to the pathogenesis of this disease, while seasonal and meteorological variations represent significant factors influencing microbial community dynamics. However, the specific pathological mechanisms mediated by microbial populations within knee joint effusions of RA patients remain poorly elucidated. The present study employs 16S rRNA high-throughput sequencing technology to characterize seasonal variation patterns affecting microbial communities in knee joint effusions of RA patients and to investigate the relationship between microbial community structures and climatic lag effects. Methods: Microbial communities in knee joint effusion samples obtained from RA patients were analyzed using 16S rRNA high-throughput sequencing methodologies. A Distributed Lag Non-linear Model (DLNM) was applied to quantify the delayed effects of climatic variables on microbial community composition. The correlation patterns between meteorological parameters and community structure were elucidated through the integration of ridge regression and redundancy analysis (RDA). Preliminary identification of potential biomarkers was conducted using random forest algorithms. Results: According to research findings, the microbial composition of knee joint effusions in RA patients shows seasonal fluctuation patterns that are compatible with those seen in RA patients, even though there is no discernible seasonal change in β-diversity. Compared with samples obtained during other seasons, spring specimens exhibited significantly elevated relative abundances of both beneficial microorganisms and opportunistic pathogenic taxa. Random forest modeling identified Escherichia-Shigella and Curtobacterium as preliminary candidate biomarkers; however, external validation is required to establish their specificity as disease indicators. Further analysis revealed that although short-term meteorological fluctuations exert minimal influence on overall microbial diversity, specific alterations in mean wind speed (MWS) and relative humidity (RH) drive compositional changes in the microbial community, manifested as rapid responses from dominant bacterial taxa and compensatory buffering effects from rare taxa. Conclusions: This study suggests that the synovial cavity microbiota in RA patients may exhibit seasonal variation patterns that are statistically associated with environmental parameters, particularly humidity and temperature. Due to the inherent limitations of the cross-sectional study design, the preliminary candidate biomarkers identified herein require validation through external cohorts. Additional investigations incorporating healthy controls and osteoarthritis (OA) cohorts are necessary to confirm specificity and to elucidate the therapeutic potential of these microbial targets for RA microbiome interventions. Currently, insufficient evidence exists to establish causal relationships among microbial populations, joint pathology, and climatic factors. Longitudinal cohort studies are imperative to validate the temporal dynamics and clinical significance of these associations. Full article

The rapid deployment of electric vehicles (EVs) has introduced new challenges to distribution networks, which are mainly related to power quality and grid reliability. Electric vehicle chargers behave as nonlinear loads because they are based on power electronic converters, which generate harmonic currents, cause voltage distortion, increase stress on network components, and might impact the overall power quality of distribution networks. In this study, power quality (PQ) measurements and harmonic characteristics were investigated for five electric vehicle models, namely the BYD Song Plus, Volkswagen ID6, Neta U, Nissan LEAF 2016, and Tesla Model 3. Measurements were carried out for different power levels—slow AC, low-power and fast AC, high-power charging modes—to evaluate the PQ characteristics and harmonic behavior of EVs. Fast charging power levels for most vehicles ranged between 5 and 11 kW, while slow charging ranged between 2.7 and 3.6 kW. It is found that harmonic characteristics, total harmonic current distortion (THD_I), and harmonic distribution depend on the EV type and the charging mode. This study found that THD_I varies between 1.5% and 10.72% for the tested EVs. Comparison with IEC power quality standards indicates that the impact of electric vehicle charging on voltage quality is limited, while current harmonic distortion varies significantly among vehicle models. Harmonic analysis reveals that the third and fifth orders dominate across most of the tested EVs, while the transition from slow to fast charging power level generally reduces low-order harmonics in most models, with vehicle-specific redistribution patterns that reflect converter topology and control strategy. The results also show that some EV chargers draw reactive power and operate with a lagging power factor, whereas other vehicles inject reactive power and operate under leading power factor conditions. Full article